


import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('0.8.3'):
						raise Exception('requires gluonnlp == 0.8.3')

if version.parse(mx.__version__) != version.parse('1.5.0'):
						raise Exception('requires mxnet == 1.5.0')

logging.set_verbosity_info()
__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							'The Nymphenburg Palace is a beautiful palace in Munich!'
def 							UpperCamelCase_(					_A :str					,  _A :str	)->		List[Any]:
			UpperCamelCase__       =							{
			    "attention_cell": "multi_head",
			    "num_layers": 4,
			    "units": 10_24,
			    "hidden_size": 7_68,
			    "max_length": 5_12,
			    "num_heads": 8,
			    "scaled": True,
			    "dropout": 0.1,
			    "use_residual": True,
			    "embed_size": 10_24,
			    "embed_dropout": 0.1,
			    "word_embed": None,
			    "layer_norm_eps": 1e-5,
			    "token_type_vocab_size": 2,
			}

			UpperCamelCase__       =							bort_4_8_768_1024_hparams

			# Let's construct the original Bort model here
			# Taken from official BERT implementation, see:
			# https://github.com/alexa/bort/blob/master/bort/bort.py
			UpperCamelCase__       =							BERTEncoder(
			    attention_cell=predefined_args["attention_cell"]					,  num_layers=predefined_args["num_layers"]					,  units=predefined_args["units"]					,  hidden_size=predefined_args["hidden_size"]					,  max_length=predefined_args["max_length"]					,  num_heads=predefined_args["num_heads"]					,  scaled=predefined_args["scaled"]					,  dropout=predefined_args["dropout"]					,  output_attention=_A					,  output_all_encodings=_A					,  use_residual=predefined_args["use_residual"]					,  activation=predefined_args.get("activation"					,  "gelu"	)					,  layer_norm_eps=predefined_args.get("layer_norm_eps"					,  _A	)					,  )

			# Vocab information needs to be fetched first
			# It's the same as RoBERTa, so RobertaTokenizer can be used later
			UpperCamelCase__       =							"openwebtext_ccnews_stories_books_cased"

			# Specify download folder to Gluonnlp's vocab
			UpperCamelCase__       =							os.path.join(get_home_dir()					,  "models"	)
			UpperCamelCase__       =							_load_vocab(_A					,  _A					,  _A					,  cls=_A	)

			UpperCamelCase__       =							nlp.model.BERTModel(
			    _A					,  len(_A	)					,  units=predefined_args["units"]					,  embed_size=predefined_args["embed_size"]					,  embed_dropout=predefined_args["embed_dropout"]					,  word_embed=predefined_args["word_embed"]					,  use_pooler=_A					,  use_token_type_embed=_A					,  token_type_vocab_size=predefined_args["token_type_vocab_size"]					,  use_classifier=_A					,  use_decoder=_A					,  )

			original_bort.load_parameters(_A					,  cast_dtype=_A					,  ignore_extra=_A	)
			UpperCamelCase__       =							original_bort._collect_params_with_prefix()

			# Build our config 🤗
			UpperCamelCase__       =							{
			    "architectures": ["BertForMaskedLM"],
			    "attention_probs_dropout_prob": predefined_args["dropout"],
			    "hidden_act": "gelu",
			    "hidden_dropout_prob": predefined_args["dropout"],
			    "hidden_size": predefined_args["embed_size"],
			    "initializer_range": 0.0_2,
			    "intermediate_size": predefined_args["hidden_size"],
			    "layer_norm_eps": predefined_args["layer_norm_eps"],
			    "max_position_embeddings": predefined_args["max_length"],
			    "model_type": "bort",
			    "num_attention_heads": predefined_args["num_heads"],
			    "num_hidden_layers": predefined_args["num_layers"],
			    "pad_token_id": 1,  # 2 = BERT, 1 = RoBERTa
			    "type_vocab_size": 1,  # 2 = BERT, 1 = RoBERTa
			    "vocab_size": len(_A	),
			}

			UpperCamelCase__       =							BertConfig.from_dict(_A	)
			UpperCamelCase__       =							BertForMaskedLM(_A	)
			hf_bort_model.eval()

			# Parameter mapping table (Gluonnlp to Transformers)
			# * denotes layer index
			#
			# | Gluon Parameter                                                | Transformers Parameter
			# | -------------------------------------------------------------- | ----------------------
			# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
			# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
			# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
			# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
			# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
			# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
			# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
			# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
			# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
			# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
			# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
			# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
			# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
			# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

			# Helper function to convert MXNET Arrays to PyTorch
			def to_torch(_A :Optional[int]	) -> nn.Parameter:
						return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()	)	)

			# Check param shapes and map new HF param back
			def check_and_map_params(_A :Optional[int]					,  _A :str	):
						UpperCamelCase__       =							hf_param.shape

						UpperCamelCase__       =							to_torch(params[gluon_param]	)
						UpperCamelCase__       =							gluon_param.shape

						assert (
						    shape_hf == shape_gluon
						), F'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

						return gluon_param

			UpperCamelCase__       =							check_and_map_params(
			    hf_bort_model.bert.embeddings.word_embeddings.weight					,  "word_embed.0.weight"	)
			UpperCamelCase__       =							check_and_map_params(
			    hf_bort_model.bert.embeddings.position_embeddings.weight					,  "encoder.position_weight"	)
			UpperCamelCase__       =							check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.bias					,  "encoder.layer_norm.beta"	)
			UpperCamelCase__       =							check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.weight					,  "encoder.layer_norm.gamma"	)

			# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
			UpperCamelCase__       =							torch.zeros_like(
			    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data	)

			for i in range(hf_bort_config.num_hidden_layers	):
						UpperCamelCase__       =							hf_bort_model.bert.encoder.layer[i]

						# self attention
						UpperCamelCase__       =							layer.attention.self

						UpperCamelCase__       =							check_and_map_params(
						    self_attn.key.bias.data					,  F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''	)

						UpperCamelCase__       =							check_and_map_params(
						    self_attn.key.weight.data					,  F'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''	)
						UpperCamelCase__       =							check_and_map_params(
						    self_attn.query.bias.data					,  F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''	)
						UpperCamelCase__       =							check_and_map_params(
						    self_attn.query.weight.data					,  F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''	)
						UpperCamelCase__       =							check_and_map_params(
						    self_attn.value.bias.data					,  F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''	)
						UpperCamelCase__       =							check_and_map_params(
						    self_attn.value.weight.data					,  F'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''	)

						# self attention output
						UpperCamelCase__       =							layer.attention.output

						UpperCamelCase__       =							check_and_map_params(
						    self_output.dense.bias					,  F'''encoder.transformer_cells.{i}.proj.bias'''	)
						UpperCamelCase__       =							check_and_map_params(
						    self_output.dense.weight					,  F'''encoder.transformer_cells.{i}.proj.weight'''	)
						UpperCamelCase__       =							check_and_map_params(
						    self_output.LayerNorm.bias					,  F'''encoder.transformer_cells.{i}.layer_norm.beta'''	)
						UpperCamelCase__       =							check_and_map_params(
						    self_output.LayerNorm.weight					,  F'''encoder.transformer_cells.{i}.layer_norm.gamma'''	)

						# intermediate
						UpperCamelCase__       =							layer.intermediate

						UpperCamelCase__       =							check_and_map_params(
						    intermediate.dense.bias					,  F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''	)
						UpperCamelCase__       =							check_and_map_params(
						    intermediate.dense.weight					,  F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''	)

						# output
						UpperCamelCase__       =							layer.output

						UpperCamelCase__       =							check_and_map_params(
						    bert_output.dense.bias					,  F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''	)
						UpperCamelCase__       =							check_and_map_params(
						    bert_output.dense.weight					,  F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''	)
						UpperCamelCase__       =							check_and_map_params(
						    bert_output.LayerNorm.bias					,  F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''	)
						UpperCamelCase__       =							check_and_map_params(
						    bert_output.LayerNorm.weight					,  F'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma'''	)

			# Save space and energy 🎄
			hf_bort_model.half()

			# Compare output of both models
			UpperCamelCase__       =							RobertaTokenizer.from_pretrained("roberta-base"	)

			UpperCamelCase__       =							tokenizer.encode_plus(_A	)["input_ids"]

			# Get gluon output
			UpperCamelCase__       =							mx.nd.array([input_ids]	)
			UpperCamelCase__       =							original_bort(inputs=_A					,  token_types=[]	)

			# Get Transformer output (save and reload model again)
			hf_bort_model.save_pretrained(_A	)
			UpperCamelCase__       =							BertModel.from_pretrained(_A	)
			hf_bort_model.eval()

			UpperCamelCase__       =							tokenizer.encode_plus(_A					,  return_tensors="pt"	)
			UpperCamelCase__       =							hf_bort_model(**_A	)[0]

			UpperCamelCase__       =							output_gluon[0].asnumpy()
			UpperCamelCase__       =							output_hf[0].detach().numpy()

			UpperCamelCase__       =							np.max(np.abs(hf_layer - gluon_layer	)	).item()
			UpperCamelCase__       =							np.allclose(_A					,  _A					,  atol=1e-3	)

			if success:
						print("✔️ Both model do output the same tensors"	)
			else:
						print("❌ Both model do **NOT** output the same tensors"	)
						print("Absolute difference is:"					,  _A	)


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--bort_checkpoint_path', default=None, type=str, required=True, help='Path the official Bort params file.'
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						__UpperCamelCase     		=							parser.parse_args()
						convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)






import numpy as np
def 							UpperCamelCase_(					_A :np.array	)->		np.array:
			return 1 / (1 + np.exp(-vector	))


if __name__ == "__main__":
						import doctest

						doctest.testmod()


